scholarly journals Automated evaluation of COVID-19 risk factors coupled with real-time, indoor, personal localization data for potential disease identification, prevention and smart quarantining

2020 ◽  
Author(s):  
J. Barabas ◽  
R. Zalman ◽  
M. Kochlan
Keyword(s):  
Risk Factors ◽  
Real Time ◽  
Automated Evaluation ◽  
Disease Identification ◽  
Localization Data

scholarly journals Do Feelings of Defeat and Entrapment Change over Time? An Investigation of the Integrated Motivational—Volitional Model of Suicidal Behaviour Using Ecological Momentary Assessments

2020 ◽  
Vol 17 (13) ◽  
pp. 4685
Author(s):  
Jana-Sophie Stenzel ◽  
Inken Höller ◽  
Dajana Rath ◽  
Nina Hallensleben ◽  
Lena Spangenberg ◽  
...  
Keyword(s):  
Risk Factors ◽  
Suicidal Ideation ◽  
Real Time ◽  
Clinical Samples ◽  
Short Term ◽  
Change Over Time ◽  
Longitudinal Association ◽  
Ecological Momentary ◽  
Over Time ◽  
Short Term Variability

(1) Background. Defeat and entrapment have been highlighted as major risk factors of suicidal ideation and behavior. Nevertheless, little is known about their short-term variability and their longitudinal association in real-time. Therefore, this study aims to investigate whether defeat and entrapment change over time and whether defeat predicts entrapment as stated by the integrated motivational–volitional model of suicidal behavior. (2) Methods. Healthy participants (n = 61) underwent a 7-day smartphone-based ecological momentary assessment (EMA) on suicidal ideation/behavior and relevant risk factors, including defeat and entrapment and a comprehensive baseline (T0) and post (T2) assessment. (3) Results. Mean squared successive differences (MSSD) and intraclass correlations (ICC) support the temporal instability as well as within-person variability of defeat and entrapment. Multilevel analyses revealed that during EMA, defeat was positively associated with entrapment at the same measurement. However, defeat could not predict entrapment to the next measurement (approximately two hours later). (4) Conclusion. This study provides evidence on the short-term variability of defeat and entrapment highlighting that repeated measurement of defeat and entrapment—preferably in real time—is necessary in order to adequately capture the actual empirical relations of these variables and not to overlook significant within-person variability. Further research—especially within clinical samples—seems warranted.

Application of artificial intelligence in university sports risk recognition and identification

2020 ◽  
pp. 1-12
Author(s):  
Ju-An Wang ◽  
Shen Liu ◽  
Xiping Zhang
Keyword(s):  
Artificial Intelligence ◽  
Risk Factors ◽  
Real Time ◽  
Risk Mitigation ◽  
Time Data ◽  
Teaching Preparation ◽  
Data Store ◽  
Teacher Factors ◽  
Risk Sources ◽  
Artificial Intelligence Technology

This article is based on artificial intelligence technology to recognize and identify risks in college sport. The application of motion recognition technology first need to collect the source data, store the collected data in the server database, collect the learner’s real-time data and return it to the database to achieve the purpose of real-time monitoring. It is found that in the identification of risk sources of sports courses, there are a total of 4 first-level risk factors, namely teacher factors, student factors, environmental factors, and school management factors, and a total of 15 second-level risk factors, which are teaching preparation, teaching process, and teaching effect. When the frequency of teaching risks is low, the consequence loss is small. When the frequency of teaching risks is low, the consequences are very serious. Risk mitigation is the main measure to reduce the occurrence of teaching risks and reduce the consequences of losses.

scholarly journals Breaking Down the Coercive Cycle: How Parent and Child Risk Factors Influence Real-Time Variability in Parental Responses to Child Misbehavior

Parenting ◽  
2016 ◽  
Vol 16 (4) ◽  
pp. 237-256 ◽  
Author(s):  
Erika Lunkenheimer ◽  
Anna Lichtwarck-Aschoff ◽  
Tom Hollenstein ◽  
Christine J. Kemp ◽  
Isabela Granic
Keyword(s):  
Risk Factors ◽  
Real Time ◽  
Time Variability ◽  
Parental Responses

scholarly journals Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2643
Author(s):  
Irfan Abbas ◽  
Jizhan Liu ◽  
Muhammad Amin ◽  
Aqil Tariq ◽  
Mazhar Hussain Tunio
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Agricultural Development ◽  
Vision System ◽  
Severe Disease ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Leaf Scorch ◽  
Disease Identification ◽  
Severe Stage

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

scholarly journals Liver Stiffness Assessed by Real-Time Two-dimensional Shear Wave Elastography Predicts Hypersplenism in Patients with Wilson’s Disease: A Prospective Study

2021 ◽  
Author(s):  
Jiajia Wang ◽  
Minxia Hu ◽  
Qiang Zhu ◽  
Lanting Sun
Keyword(s):  
Risk Factors ◽  
Prospective Study ◽  
Real Time ◽  
Shear Wave ◽  
Wilson’S Disease ◽  
Liver Stiffness ◽  
Shear Wave Elastography ◽  
Wilson's Disease ◽  
Dynamic Monitoring ◽  
Two Dimensional

Abstract Background To explore the value of liver stiffness assessed by two-dimensional real-time shear wave elastography (2D-SWE）in predicting the occurrence of hypersplenism in patients diagnosed with Wilson’s disease (WD). Methods A total of 90 WD patients were enrolled in this prospective study between May 2018 and December 2018. Clinical data and ultrasound imaging including 2D-SWE liver stiffness of WD patients as baseline data were collected. Patients were followed up for 24 months, or patients developed hypersplenism after enrollment. Risk factors for hypersplenism were determined using cox regression and receiver operating characteristic curve. Results Twenty-night (32.2%) patients were found developed hypersplenism. The age, the diameter of portal vein, and the liver stiffness were independent risk factors associated with hypersplenism in WD. The cutoff value of liver stiffness for predicting hypersplenism was 10.45 kPa, with sensitivity and specificity of 75.9% and 73.8%, respectively. When patients were divided into two groups according to liver stiffness ≥10.45 kPa or <10.45 kPa, the incidence of hypersplenism were 57.9% vs. 13.5% (P<0.001), and the median time between the enrollment and the development of hypersplenism was 15 months vs. 22 months (P<0.001) for the two groups, respectively. Conclusion The liver stiffness measured by 2D-SWE was a reliable predictor of hypersplenism in WD patients. Dynamic monitoring WD patients using 2D-SWE is crucial for the early diagnosis of hypersplenism.

scholarly journals Seroprevalence and Molecular Identification of Brucella spp. in Bovines in Pakistan—Investigating Association With Risk Factors Using Machine Learning

2020 ◽  
Vol 7 ◽  
Author(s):  
Aman Ullah Khan ◽  
Falk Melzer ◽  
Ashraf Hendam ◽  
Ashraf E. Sayour ◽  
Iahtasham Khan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Developing Countries ◽  
Real Time ◽  
Molecular Identification ◽  
Real Time Pcr ◽  
Ring Test ◽  
Test Results ◽  
Bovine Brucellosis ◽  
Milk Samples

Bovine brucellosis is a global zoonosis of public health importance. It is an endemic disease in many developing countries including Pakistan. This study aimed to estimate the seroprevalence and molecular detection of bovine brucellosis and to assess the association of potential risk factors with test results. A total of 176 milk and 402 serum samples were collected from cattle and buffaloes in three districts of upper Punjab, Pakistan. Milk samples were investigated using milk ring test (MRT), while sera were tested by Rose–Bengal plate agglutination test (RBPT) and indirect enzyme-linked immunosorbent assay (i-ELISA). Real-time PCR was used for detection of Brucella DNA in investigated samples. Anti-Brucella antibodies were detected in 37 (21.02%) bovine milk samples using MRT and in 66 (16.4%) and 71 (17.7%) bovine sera using RBPT and i-ELISA, respectively. Real-time PCR detected Brucella DNA in 31 (7.71%) from a total of 402 bovine sera and identified as Brucella abortus. Seroprevalence and molecular identification of bovine brucellosis varied in some regions in Pakistan. With the use of machine learning, the association of test results with risk factors including age, animal species/type, herd size, history of abortion, pregnancy status, lactation status, and geographical location was analyzed. Machine learning confirmed a real observation that lactation status was found to be the highest significant factor, while abortion, age, and pregnancy came second in terms of significance. To the authors' best knowledge, this is the first time to use machine learning to assess brucellosis in Pakistan; this is a model that can be applied for other developing countries in the future. The development of control strategies for bovine brucellosis through the implementation of uninterrupted surveillance and interactive extension programs in Pakistan is highly recommended.

scholarly journals HIV prevalence and risk factors in infants born to HIV positive mothers, measured by dried blood spot real-time PCR assay in Tigray, Northern Ethiopia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Mulu Lemlem Desta ◽  
Muthupandian Saravanan ◽  
Haftamu Hilekiros ◽  
Atsebaha Gebrekidan Kahsay ◽  
Nesredin Futwi Mohamed ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real Time ◽  
Real Time Pcr ◽  
Hiv Prevalence ◽  
Dried Blood Spot ◽  
Hiv Positive ◽  
Northern Ethiopia ◽  
Pcr Assay ◽  
Hiv Positive Mothers ◽  
Blood Spot
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